Identification and Validation of a Reliable Prognostic Eleven-Genes Signature for Hepatocellular Carcinoma

Abstract

Background: Hepatocellular carcinoma (HCC) is a common malignant tumor with high mortality and mortality. Although advances in early diagnosis, disease management and treatment of HCC, the outcomes remain unsatisfactory. This study aimed to identify the reliable prognostic biomarkers based integrated bioinformatics analysis to predict and improve the survival of HCC patients. Methods: The gene expression or transcriptome profiles and survival of HCC were acquired from the Gene Expression Omnibus database (GEO) and the Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were screened out by the limma or edgeR package in the R software. Univariate, LASSO and multivariate Cox regression analyses were conducted to explore survival-related signature. Subsequently, a prognostic model and nomogram composed of prognostic signature were constructed for assessing overall survival (OS). Kaplan-Meier analysis, receiver operating characteristic (ROC) curve and stratified analysis were performed to confirm the prognostic performance of the prognostic model.Results: Compared with nontumor samples, 451 reliable DEGs were identified using the robust rank aggregation and overlap validation. Eleven survival-related DEGs were selected for the construction of a risk evaluation model, which could efficiently distinguish high-risk patients from low-risk patients and even be feasible in the subgroups of stages and age. Further analyses suggested the positive and independent prognostic performance of the model compared to other clinical characteristics (P< 0.05, ROC > 0.7). Finally, a prognostic nomogram composed of the model was constructed for assessing the overall survival, and Harrell’s concordance index and calibration curves demonstrated its efficient predictive performance. Conclusion: The predictive model and nomogram will contribute directly to further clinical applications in the individualized survival prediction, the improvement of treatment strategies and more accurate management for patients with HCC.